There are multiple techniques for probabilistic regression in the strictest sense - that is, giving a precise probability density function for the target value given the features. NGBoost ([1]}) is the most direct analog to the method presented here, as it is simple to implement, usable by non-experts, and also relies on gradient boosting. However, our method is nonparametric, while NGBoost requires a parametric specification and then predicts the value of the parameters given the features. Generalized Additive Models for Shape, Scale, and Location (GAMLSS) ([2]}) are similarly restricted to a specified model form. Bayesian approaches such as Bayesian Additive Regression Trees (BART) ( [3]}) require computationally expensive methods to sample the posterior distribution and are generally difficult to use. Bayesian Deep Learning methods (e.g. [4]}, [5]}, [6]}) have shown some promise but are also relatively difficult to use.
